Stage II consists of using six features (not including lymph node status)

in a support vector machine (SVM) classifier to classify

a given patient into one of these three groups which

we are able to achieve with 82.7% tenfold cross validation correctness.

Important findings include the following:

1. The good group consists of 69 patients all without chemotherapy.

2. The poor group consists of 73 patients all with chemotherapy

3. The intermediate group consists of 44 patients without chemotherapy

and 67 with chemotherapy.

4. Pairwise p-values, based

on the logrank statistic, for the distinct survival curves for the

three groups above is no greater than 0.0076.

5. The intermediate group's 67 patientswith

chemotherapy

have abetter

survival curve than the group's 44 patients

without

chemotherapy. The p-value for this pair of distinct

survival curves is 0.0306. Furthermore, the survival curve of

the 67 patients with chemotherapy in this group is not significantly

different (p-value 0.0817) from the good group survival curve.

Ofparticular significance

is the last item above, because we have

identified a classifiable intermediate group, for which patients

with chemotherapy do better than those without chemotherapy,

which is the reverse of that for the overall population of 253 patients.

ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/01-03.ps

Mass Collaboration and Data Mining

Raghu Ramakrishnan

Mass Collaboration is a new "P2P"-style approach to large-scale knowledgesharing, with applications in customer support, focused community development,and capturing knowledge distributed within large organizations. Effectivelysupporting this paradigm raises many technical challenges, and offers intriguingopportunities for mining massive amounts of data captured continually from userinteractions. Data mining offers the promise of increased business intelligence,and also improved user experiences, leading to increased participation andgreater quality in the knowledge that is captured, both of which are centralobjectives in Mass Collaboration. In this talk, I will introduce MassCollaboration and discuss some important data mining tasks motivated by thisparadigm.